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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- image-to-text
+---
+
+# PARSeq tiny v1.0
+
+PARSeq model pre-trained on various real [STR datasets](https://github.com/baudm/parseq/blob/main/Datasets.md) at image size 128x32 with a patch size of 8x4.
+
+## Model description
+
+PARSeq (Permuted Autoregressive Sequence) models unify the prevailing modeling/decoding schemes in Scene Text Recognition (STR). In particular, with a single model, it allows for context-free non-autoregressive inference (like CRNN and ViTSTR), context-aware autoregressive inference (like TRBA), and bidirectional iterative refinement (like ABINet).
+
+![model image](https://github.com/baudm/parseq/raw/main/.github/system.png)
+
+## Intended uses & limitations
+
+You can use the model for STR on images containing Latin characters (62 case-sensitive alphanumeric + 32 punctuation marks).
+
+### How to use
+
+*TODO*
+
+### BibTeX entry and citation info
+
+```bibtex
+@InProceedings{bautista2022parseq,
+  author={Bautista, Darwin and Atienza, Rowel},
+  title={Scene Text Recognition with Permuted Autoregressive Sequence Models},
+  booktitle={Proceedings of the 17th European Conference on Computer Vision (ECCV)},
+  month={10},
+  year={2022},
+  publisher={Springer International Publishing},
+  address={Cham}
+}
+```

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